In order to make machine learning models "fair", works aim at modifying the feature representations of the data [1]}, the class label annotations [2]} or the data itself [3]}.
Learning this latent representation includes an additional cross entropy classification loss [4]}, a decomposition loss [3]}, an additional hidden layer for adversarial optimization [6]}, distribution matching [7]}, using Variational Autoencoders [8]}, or by learning the representation as an adversarial minimax game [9]}. The goal is not only to improve fairness but also to interpret how fairness is enforced.
Such methods build on special network architectures [10]}, [11]} or a combination of different machine learning algorithms [12]}.
For the domain adaptation task approaches make use of re-weighting the source samples to better match the target domain [13]}, [14]}, learning shared weights [15]} or a common subspace [16]}, modifying the network architecture [17]}, [18]}, or using Generative Adversarial Networks [8]}, [20]}. Interestingly, if causal aspects are taken into account, predictions can be improved [21]}, [22]}.
In the same line, recent work aims for analysing those areas in a common way [8]}, [24]}, [25]}.
